Your SlideShare is downloading. ×
Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Establishing empirical relations to predict grain size and hardness of pulsed current micro plasma arc welded SS 304L sheets

345
views

Published on

SS 304L, an austenitic Chromium-Nickel stainless steel offering the optimum combination of corrosion resistance, strength and ductility, is favorable for many mechanical components. The low carbon …

SS 304L, an austenitic Chromium-Nickel stainless steel offering the optimum combination of corrosion resistance, strength and ductility, is favorable for many mechanical components. The low carbon content reduces susceptibility to carbide precipitation during welding. In case of single pass welding of thinner section of this alloy, pulsed current micro plasma arc welding was found beneficial due to its advantages over the conventional continuous current process. The paper focuses on developing mathematical models to predict grain size and hardness of pulsed current micro plasma arc welded SS304L joints. Four factors, five level, central composite rotatable design matrix is used to optimize the number of experiments. The mathematical models have been developed by response surface method. The adequacy of the models is checked by ANOVA technique. By using the developed mathematical models, grain size and hardness of the joints can be predicted with 99% confidence level. Contour plots are drawn to study the interaction effect of pulsed current micro plasma arc welding parameters on fusion zone grain size and hardness of SS304L steel.

Published in: Technology, Business

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
345
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 2012 American Transactions on Engineering & Applied Sciences American Transactions on Engineering and Applied Sciences http://TuEngr.com/ATEAS, http://Get.to/Research Establishing Empirical Relations to Predict Grain Size and Hardness of Pulsed Current Micro Plasma Arc Welded SS 304L Sheets a* b Kondapalli Siva Prasad , Chalamalasetti Srinivasa Rao , and c Damera Nageswara Raoa Department of Mechanical Engineering, Anil Neerukonda Institute of Technology and Sciences,Visakhapatnam, INDIAb Department of Mechanical Engineering, Andhra University,Visakhapatnam, INDIAc Centurion University of Technology & Management, Odisha, INDIAARTICLEINFO A B S T RA C TArticle history: SS 304L, an austenitic Chromium-Nickel stainless steelReceived 23 August 2011Received in revised form offering the optimum combination of corrosion resistance, strength01 December 2011 and ductility, is favorable for many mechanical components. TheAccepted 25 December 2011 low carbon content reduces susceptibility to carbide precipitationAvailable online26 December 2011 during welding. In case of single pass welding of thinner section ofKeywords: this alloy, pulsed current micro plasma arc welding was foundPulsed current micro plasma beneficial due to its advantages over the conventional continuousarc welding, current process. The paper focuses on developing mathematicalSS304L, models to predict grain size and hardness of pulsed current micrograin size, plasma arc welded SS304L joints. Four factors, five level, centralhardness, composite rotatable design matrix is used to optimize the number ofDesign of Experiments, experiments. The mathematical models have been developed byANOVA. response surface method. The adequacy of the models is checked by ANOVA technique. By using the developed mathematical models, grain size and hardness of the joints can be predicted with 99% confidence level. Contour plots are drawn to study the interaction effect of pulsed current micro plasma arc welding parameters on fusion zone grain size and hardness of SS304L steel. 2012 American Transactions on Engineering and Applied Sciences.*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 57at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 2. 1. Introduction    In welding processes, the input parameters have greater influence on the mechanical propertiesof the weld joints. By varying the input process parameters, the output could be changed withsignificant variation in their mechanical properties. Accordingly, welding is usually selected to geta welded joint with excellent mechanical properties. To determine these welding combinations thatwould lead to excellent mechanical properties, different methods and approaches have been used.Various optimization methods can be applied to define the desired output variables throughdeveloping mathematical models to specify the relationship between the input parameters andoutput variables. One of the most widely used methods to solve this problem is response surfacemethodology (RSM), in which the unknown mechanism with an appropriate empirical model isapproximated, being the function of representing a response surface method Welding thin sheets is quite different from welding thick sections, because during welding ofthin sheets many problems are experienced. These problems are usually linked with heat input.Fusion welding generally involves joining of metals by application of heat for melting of metals tobe joined. Almost all the conventional arc welding processes offer high heat input, which in turnleads to various problems such as burn through or melt trough, distortion, porosity, bucklingwarping and twisting of welded sheets, grain coarsening , evaporation of useful elements presentin coating of the sheets, joint gap variation during welding, fume generation form coated sheets etc.Use of proper welding process, procedure and technique is one tool to address this issue(Balasubramanian et.al, 2010). Micro Plasma arc Welding (MPAW) is a good process for joiningthin sheet, but it suffers high equipment cost compared to GTAW. However it is more economicalwhen compare with Laser Beam welding and Electron Beam Welding processes. Pulsed current MPAW involves cycling the welding current at selected regular frequency. Themaximum current is selected to give adequate penetration and bead contour, while the minimum isset at a level sufficient to maintain a stable arc (Balasubramanian et.al, 2006 and Madusudhanaet.al, 1997). This permits arc energy to be used effectively to fuse a spot of controlled dimensionsin a short time producing the weld as a series of overlapping nuggets. By contrast, in constantcurrent welding, the heat required to melt the base material is supplied only during the peak currentpulses allowing the heat to dissipate into the base material leading to narrower heat affected zone 58 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 3. (HAZ). Advantages include improved bead contours, greater tolerance to heat sink variations,lower heat input requirements, reduced residual stresses and distortion, refinement of fusion zonemicrostructure and reduced with of HAZ. There are four independent parameters that influence theprocess are peak current, back current, pulse and pulse width. From the literature review (Zhang and Niu, 2000, Sheng-Chai Chi and LI-Chang Hsu, 2001,Hsiao et.al, 2008, Siva et.al, 2008, Lakshinarayana et.al, 2008, Balasubramanian et.al, 2009,Srimath and Muragan, 2011) it is understood that in most of the works reported the effect ofwelding current, arc voltage, welding speed, wire feed rate, magnitude of ion gas flow, torchstand-off, plasma gas flow rate on weld quality characteristics like front melting width, backmelting width, weld reinforcement, welding groove root penetration, welding groove width,front-side undercut are considered. However much effort was not made to develop mathematicalmodels to predict the same especially when welding thin sheets in a flat position. Hence anattempt is made to correlate important pulsed current MPAW process parameters to grain size andhardness of the weld joints by developing mathematical models by using statistical tools such asdesign of experiments, analysis of variance and regression analysis.2. Literature review on Response Surface Method  Response Surface Method or commonly known as RSM is an anthology of statistical andmathematical methods that are helpful in generating improved methods and optimizing a weldingprocess. RSM is more frequently used in analyzing the relationships and the influences of inputparameters on the responses. The method was introduced by G. E. P. Box and K. B. Wilson in1951. The main idea of RSM is to use a set of designed experiments to obtain an optimal response.Box and Wilson used first-degree polynomial model to obtain DOE through RSM andacknowledged that the model is only an approximation and is easy to estimate and apply, evenwhen little information is known about the process. Response Surface Regression method is anassortment of mathematical and statistical techniques useful for modeling and analyzingexperiments in which a response variable is influenced by several independent variables. Itexplores the relationships between several independent variables and one or more response*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 59at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 4. variables; the response variable can be graphically viewed as a function of the process variables (orindependent variables) and this graphical perspective of the problem has led to the term ResponseSurface Method (Myers and Montgomery, 2002). RSM is applied to fit the acquired model to thedesired model when random factors are present and it may fit linear or quadratic models to describethe response in terms of the independent variables and then search for the optimal settings for theindependent variables by performing an optimization step. According to (Clurkin and Rosen,2002), the RSM was constructed to check the model part accuracy which uses the build time asfunction of the process variables and other parameters. According to (Asiabanpour et.al, 2006)developed the regression model that describes the relationship between the factors and thecomposite desirability. RSM also improves the analyst’s understanding of the sensitivity betweenindependent and dependent variables (Bauer et.al, 1999). With RSM, the relationship between theindependent variables and the responses can be quantified (Kechagias, 2007). RSM is anexperimental strategy and have been employed by research and development personnel in theindustry, with considerable success in a wide variety of situations to obtain solutions forcomplicated problems. The following two designs are widely used for fitting a quadratic model in RSM.2.1 Central Composite Designs  Central composite designs (CCDs), also known as Box-Wilson designs, are appropriate forcalibrating the full quadratic models described in Response Surface Models. There are three typesof CCDs, namely, circumscribed, inscribed and faced. The geometry of CCD’s is shown in theFigure 1. Figure 1: Circumscribed, inscribed and faced designs. 60 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 5. Each design consists of a factorial design (the corners of a cube) together with center and starpoints that allow estimation of second-order effects. For a full quadratic model with n factors,CCDs have enough design points to estimate the (n+2)(n+1)/2 coefficients in a full quadraticmodel with n factors. The type of CCD used (the position of the factorial and star points) is determined by thenumber of factors and by the desired properties of the design. Table 1 summarizes someimportant properties. A design is rotatable if the prediction variance depends only on the distanceof the design point from the center of the design. Table 1: Comparison of CCD’s.Design Rotatable Factor Uses Accuracy of Estimates Levels Points Outside ±1Circumscribed Yes 5 Yes Good over entire design space(CCC)Inscribed Yes 5 No Good over central subset of design space(CCI)Faced (CCF) No 3 No Fair over entire design space; poor for pure quadratic coefficients2.2 Box­Behnken Designs  Box-Behnken designs (Figure 2) are used to calibrate full quadratic models. These arerotatable and for a small number of factors (four or less), require fewer runs than CCDs. Byavoiding the corners of the design space, they allow experimenters to work around extreme factorcombinations. Like an inscribed CCD, however, extremes are then poorly estimated. Figure 2: Box-Behnken design*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 61at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 6. 3. Experimental Procedure  Austenitic stainless steel (SS304L) sheets of 100 x 150 x 0.25mm are welded autogenouslywith square butt joint without edge preparation. The chemical composition of SS304L stainlesssteel sheet is given in Table 2. High purity argon gas (99.99%) is used as a shielding gas and atrailing gas right after welding to prevent absorption of oxygen and nitrogen from the atmosphere.The welding has been carried out under the welding conditions presented in Table 3. From theliterature (Balasubramaniam et.al, 2007, Balasubramaniam et.al, 2008, Balasubramaniam et.al,2009, Balasubramaniam et.al, 2010) it is understood that in pulsed current arc welding processes,four important factors namely peak current, back current, pulse and pulse width are dominatingover other factors. In the present work the above four factors of pulsed current MPAW are chosenand their values are presented in Table 4. A large number of trail experiments were carried outusing 0.25mm thick SS304L sheets to find out the feasible working limits of pulsed current MPAWprocess parameters. Due to wide range of factors, it has been decided to use four factors, fivelevels, rotatable central composite design matrix to perform the number of experiments forinvestigation. Table 5 indicates the 31 set of coded conditions used to form the design matrix. Thefirst sixteen experimental conditions (rows) have been formed for main effects. The next eightexperimental conditions are called as corner points and the last seven experimental conditions areknown as center points. The method of designing such matrix is dealt elsewhere (Montgomery,1991, Box et.al,1978). For the convenience of recording and processing the experimental data, theupper and lower levels of the factors are coded as +2 and -2, respectively and the coded values ofany intermediate levels can be calculated by using Equation (1) (Ravindra and Parmar, 1987). Xi = 2[2X-(Xmax + Xmin)] / (Xmax – Xmin) (1) Where Xi is the required coded value of a parameter X. The X is any value of the parameterfrom Xmin to Xmax, where Xmin is the lower limit of the parameter and Xmax is the upper limit of theparameter. Table 2: Chemical composition of SS304L (weight %). C Si Mn P S Cr Ni Mo Ti N 0.021 0.35 1.27 0.030 0.001 18.10 8.02 -- -- 0.053 62 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 7. Table 3: Welding conditions. Power source Secheron Micro Plasma Arc Machine (Model: PLASMAFIX 50E) Polarity DCEN Mode of operation Pulse mode Electrode 2% thoriated tungsten electrode Electrode Diameter 1mm Plasma gas Argon and Hydrogen Plasma gas flow rate 6 Lpm Shielding gas Argon Shielding gas flow rate 0.4 Lpm Purging gas Argon Purging gas flow rate 0.4 Lpm Copper Nozzle diameter 1mm Nozzle to plate distance 1mm Welding speed 260mm/min Torch Position Vertical Operation type Automatic Table 4: Important factors and their levels. LevelsSI No Input Factor Units -2 -1 0 +1 +2 1 Peak Current Amps 6 6.5 7 7.5 8 2 Back Current Amps 3 3.5 4 4.5 5 3 Pulse No’s/sec 20 30 40 50 60 4 Pulse width % 30 40 50 60 704. Recording the responses 4.1 Measurement of grain size  Three metallurgical samples are cut from each joint, with the first sample being located at25mm behind the trailing edge of the crater at the end of the weld and mounted using Bakelite.Sample preparation and mounting is done as per ASTM E 3-1 standard. The samples are surfacegrounded using 120 grit size belt with the help of belt grinder, polished using grade 1/0 (245 meshsize), grade 2/0( 425 mesh size) and grade 3/0 (515 mesh size) sand paper. The specimens arefurther polished by using aluminum oxide initially and the by utilizing diamond paste and velvet*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 63at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 8. cloth in a polishing machine. The polished specimens are etched by using 10% Oxalic acid solutionto reveal the microstructure as per ASTM E407. Micrographs are taken using metallurgicalmicroscope (Make: Carl Zeiss, Model: Axiovert 40MAT) at 100X magnification. The micrographsof parent metal zone and weld fusion zone are shown in Figures 3 and 4. Table 5: Design matrix and experimental results. SI No Peak Current Back current Pulse Pulse width Grain Size Hardness (Amps) (Amps) (No/sec) (%) (Micons) (VHN) 1 -1 -1 -1 -1 20.812 198 2 1 -1 -1 -1 30.226 190 3 -1 1 -1 -1 21.508 199 4 1 1 -1 -1 27.536 193 5 -1 -1 1 -1 27.323 193 6 1 -1 1 -1 25.206 195 7 -1 1 1 -1 25.994 195 8 1 1 1 -1 23.491 197 9 -1 -1 -1 1 26.290 194 10 1 -1 -1 1 29.835 190 11 -1 1 -1 1 20.605 200 12 1 1 -1 1 27.764 193 13 -1 -1 1 1 30.095 190 14 1 -1 1 1 26.109 194 15 -1 1 1 1 27.385 193 16 1 1 1 1 25.013 195 17 -2 0 0 0 20.788 196 18 2 0 0 0 25.830 195 19 0 -2 0 0 31.663 188 20 0 2 0 0 27.263 193 21 0 0 -2 0 25.270 195 22 0 0 2 0 26.030 194 23 0 0 0 -2 24.626 195 24 0 0 0 2 26.626 194 25 0 0 0 0 24.845 196 26 0 0 0 0 24.845 196 27 0 0 0 0 20.145 200 28 0 0 0 0 24.845 195 29 0 0 0 0 20.045 201 30 0 0 0 0 24.845 195 31 0 0 0 0 20.445 198 Grain size of parent metal and weld joint is measured by using Scanning Electron Microscope(Make: INCA Penta FETx3, Model:7573). Figure 5 and Figure 6 indicates the measurement ofgrain size for parent metal zone and weld fusion zone. Average values of grain size are presentedin Table 5. 64 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 9. Figure 3: Microstructure of parent metal zone Figure 4: Microstructure of weld fusion zone. Figure 5: Grain size of parent metal. Figure 6: Grain size of weld fusion zone. The grain size at the weld fusion zone is smaller than parent metal zone, which indicates soundweld joint.4.2 Measurement of hardness  Vickers’s micro hardness testing machine (Make: METSUZAWA CO LTD, JAPAN, Model:MMT-X7) was used to measure the hardness at the weld fusion zone by applying a load of 0.5Kg asper ASTM E384. Average values of three samples of each test case are presented in Table 5.5. Developing mathematical models  In most RSM problems (Cochran and Cox, 1957, Barker, 1985, Montgomery,1991, Gardinerand Gettinby,1998), the form of the relationship between the response (Y) and the independentvariables is unknown. Thus the first step in RSM is to find a suitable approximation for the true*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 65at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 10. functional relationship between the response and the set of independent variables. Usually, a low order polynomial is some region of the independent variables is employed. Ifthe response is well modeled by a linear function of the independent variables then theapproximating function in the first order model. Y = bo+∑bi xi +∈ (2) If interaction terms are added to main effects or first order model, then we have a modelcapable of representing some curvature in the response function. Y = bo+∑bi xi + ∑∑bijxixj+∈ (3) The curvature, of course, results from the twisting of the plane induced by the interaction termβijxixj Table 6: Estimated Regression Coefficients for grain size. Term Coef SE Coef T P Remarks Constant 22.8593 0.6453 35.424 0.000 Significant Peak Current 1.0522 0.3485 3.019 0.008 Significant Back Current -1.0583 0.3485 -3.037 0.008 Significant Pulse 0.3150 0.3485 0.904 0.379 Insignificant Pulse Width 0.6250 0.3485 1.793 0.092 Insignificant Peak Current*Peak Current 0.1020 0.3193 0.320 0.753 Insignificant Back Current*Back Current 1.6405 0.3193 5.138 0.000 Significant Pulse*Pulse 0.6873 0.3193 2.153 0.047 Insignificant Pulse Width*Pulse Width 0.6813 0.3193 2.134 0.049 Insignificant Peak Current*Back Current 0.0910 0.4268 0.213 0.834 Insignificant Peak Current*Pulse -2.3203 0.4268 -5.436 0.000 Significant Peak Current*Pulse Width -0.4047 0.4268 -0.948 0.357 Insignificant Back Current*Pulse 0.1813 0.4268 0.425 0.677 Insignificant Back Current*Pulse Width -0.4078 0.4268 -0.955 0.354 Insignificant Pulse*Pulse Width 0.1360 0.4268 0.319 0.754 Insignificant S = 1.707 R-Sq = 84.2% R-Sq(adj) = 70.4% There are going to be situations where the curvature in the response function is not adequatelymodeled by Equation-3. In such cases, a logical model to consider is 66 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 11. Y = bo+∑bi xi +∑biixi2 + ∑∑bijxixj+∈ (4) Where bii repesent pure second order or quadratic effects. Equation 4 is a second orderresponse surface model. Using MINITAB 14 statistical software package, the significant coefficients were determinedand final models are developed using significant coefficients to estimate grain size and hardnessvalues of weld joint. The details of estimation of regression coefficients for grain size andhardness are presented in Tables 6 and 7. Table 7: Estimated Regression Coefficients for hardness. Term Coef SE Coef T P Remarks Constant 197.286 0.6410 307.801 0.000 Significant Peak Current -0.708 0.3462 -2.046 0.058 Insignificant Back Current 1.292 0.3462 3.731 0.002 Significant Pulse -0.292 0.3462 -0.843 0.412 Insignificant Pulse Width -0.542 0.3462 -1.565 0.137 Insignificant Peak Current*Peak Current -0.353 0.3171 -1.112 0.283 Insignificant Back Current*Back Current -1.603 0.3171 -5.054 0.000 Significant Pulse*Pulse -0.603 0.3171 -1.900 0.076 Insignificant Pulse Width*Pulse Width -0.603 0.3171 -1.900 0.076 Insignificant Peak Current*Back Current -0.188 0.4240 -0.442 0.664 Insignificant Peak Current*Pulse 2.188 0.4240 5.160 0.000 Significant Peak Current*Pulse Width 0.312 0.4240 0.737 0.472 Insignificant Back Current*Pulse -0.313 0.4240 -0.737 0.472 Insignificant Back Current*Pulse Width 0.313 0.4240 0.737 0.472 Insignificant Pulse*Pulse Width -0.313 0.4240 -0.737 0.472 Insignificant S = 1.696 R-Sq = 83.2% R-Sq(adj) = 68.5% The final mathematical models are given in terms of grain size and hardness as below:Grain Size (G) G = 22.859+1.052X1-1.058X2+0.315X3+0.625X4+1.640X22-2.320X1X3 (5)*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 67at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 12. Hardness (H) H = 197.286-0.708X1+1.292X2-0.292X3-0.542X4-1.603X22+2.188X1X3 (6) Where X1, X2, X3 and X4 are the coded values of peak current, back current, pulse and pulsewidth. Table 8: ANOVA test results for grain size and hardness. Grain Size Source DF Seq SS Adj SS Adj MS F P Regression 14 249.023 249.023 17.7873 6.10 0.000 Linear 4 65.207 65.207 16.3018 5.59 0.005 Square 4 91.443 91.443 22.8608 7.84 0.001 Interaction 6 92.372 92.372 15.3954 5.28 0.004 Residual Error 16 46.639 46.639 2.9149 Lack-of-Fit 10 9.750 9.750 0.9750 0.16 0.994 Pure Error 6 36.889 36.889 6.1481 Total 30 295.661 Hardness Source DF Seq SS Adj SS Adj MS F P Regression 14 228.18 228.18 16.299 5.67 0.001 Linear 4 61.17 61.17 15.292 5.32 0.006 Square 4 83.64 83.64 20.910 7.27 0.002 Interaction 6 83.38 83.38 13.896 4.83 0.005 Residual Error 16 46.01 46.01 2.876 Lack-of-Fit 10 10.58 10.58 1.058 0.18 0.991 Pure Error 6 35.43 35.43 5.905 Total 30 274.19 Table value of Fisher’s ratio is 7.87 for 99% confidence level Where DF =Degrees of Freedom, SS=Sum of Squares, F=Fisher’s ratio6. Checking the adequacy of the developed models  The adequacy of the developed models was tested using the analysis of variance technique(ANOVA). As per this technique, if the calculated value of the Fratio of the developed model is lessthan the standard Fratio (from F-table) value at a desired level of confidence (say 99%), then themodel is said to be adequate within the confidence limit. ANOVA test results are presented inTable 8 for all the models. From the table it is understood that the developed mathematical modelsare found to be adequate at 99% confidence level. Coefficient of determination ‘ R2 ’ is used tofind how close the predicted and experimental values lie. The value of ‘ R2 ’ for the above 68 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 13. developed models is found to be about 0.84, which indicates good correlation exists between theexperimental values and predicted values. Figures 7 and 8 indicate the scatter plots for grain size and hardness of the weld joint andreveals that the actual and predicted values are close to each other with in the specified limits. To validate the developed models further, one has to conduct validation tests and check forrepeatability. However in the present paper confirmation test results are not implemented. Scatterplot of Grain Size Scatterplot of Hardness 32 202 200 30 198 28 196 Actual Actual 26 194 24 192 22 190 20 188 20 22 24 26 28 30 32 189.0 190.5 192.0 193.5 195.0 196.5 198.0 199.5 Predicted Predicted Figure 7: Scatter plot of Grain Size Figure 8: Scatter plot of Hardness Main Effects Plot for Grain Size Main Effects Plot for Hardness Peak Current Back Current Peak Current Back Current 196 30.0 194 27.5 192 25.0 190 22.5 20.0 188 6.0 6.5 7.0 7.5 8.0 3.0 3.5 4.0 4.5 5.0 6.0 6.5 7.0 7.5 8.0 3.0 3.5 4.0 4.5 5.0 Pulse Pulse Width Pulse Pulse Width 196 30.0 194 27.5 192 25.0 190 22.5 20.0 188 20 30 40 50 60 30 40 50 60 70 20 30 40 50 60 30 40 50 60 70 Figure 9: Variation of grain size. Figure: 10 Variation of hardness.*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 69at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 14. 7. Effect of process variable on output responses 7.1 Main effect    The variation of grain size and hardness of SS304L welds with pulsed current MPAW inputprocess parameters are presented in Figures 9 and 10. From Figures 9 and 10 it is clearly understood that grain size and hardness are inverselyproportional, i.e. smaller the grain size, higher the hardness of the weld joint.7.2 Interaction effects  Contour plots play a very important role in the study of the response surface. By generatingcontour plots using software (MINITAB14) for response surface analysis, the optimum is locatedby characterizing the shape of the surface. If the counter patterning of circular shaped countersoccurs, it tends to suggest the independence of factor effects; while elliptical contours may indicatefactor interaction. Figures 11a and 11b represent the contour plots for grain size and Figures 11aand 11b represents the contour plots for hardness. From the contour plots, the interaction effect between the input process parameters and outputresponse can be clearly analysed. Contour Plot of Grain Size vs Back Current, Peak Current Contour Plot of Grain Size vs Pulse Width, Pulse 5.0 70 Hold Values 28.5 Hold Values 28 24 Pulse 40 25.5 Peak Current 7 Pulse Width 50 Back Current 4 26 27.0 4.5 60 Back Current Pulse Width 4.0 50 22 3.5 40 28 26 30 25.5 24.0 3.0 30 6.0 6.5 7.0 7.5 8.0 20 30 40 50 60 Peak Current Pulse Figure 10a: Contour plot of Grain Size Figure 10b: Contour plot of Grain Size (Peak current, Back current) (Pulse, Pulse width) From Figures 10a and 10b it is understood that the grain size is more sensitive to changes inpulse and pulse width than to changes in peak current and back current. Also from Figure 10a, thegrain size is more sensitive to changes in peak current than changes in pulse and pulse width. 70 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 15. Contour Plot of Hardness vs Back Current, Peak Current Contour Plot of Hardness vs Pulse Width, Pulse 5.0 70 Hold Values Hold Values 194 192 Pulse 40 Peak Current 7 Pulse Width 50 Back Current 4 196 4.5 60 194 Back Current Pulse Width 4.0 50 196 194 3.5 40 192 190 3.0 30 6.0 6.5 7.0 7.5 8.0 20 30 40 50 60 Peak Current Pulse Figure 11a: Contour plot of Hardness Figure 11b: Contour plot of Hardness (Peak current, Back current) (Pulse, Pulse width) From Figures 11a and 11b it is understood that the hardness is more sensitive to changes inpulse and pulse width than to changes in peak current and back current. Also from Figure 11a, thehardness is more sensitive to changes in peak current than changes in pulse and pulse width. From the contour plots of grain size and hardness, it is understood that peak current and pulseplays a major role in deciding the grain size and hardness of the weld joint. The decrease inhardness is the result of the increased input heat associated with the use of higher peak current. Theformation of coarse grains in the fusion zone is responsible for the lower hardness of the weldjoints. Also increase in heat input results in slow cooling rate, which also contributes to longer timefor grain coarsening. The increase in hardness is because of grain refinement at fusion zone causedby using pulsing current.8. Conclusions  Empirical relations are developed to predict grain size and hardness of pulsed current microplasma arc welded SS304L sheets using response surface method. The developed model can beeffectively used to predict grain size and hardness of pulsed current micro plasma arc welded jointsat 99% confidence level. Contour plots are drawn and analysed that grain size and hardness aremore sensitive to peak current and pulse. Peak current is most important parameter as it affects thegrain size which signifies the hardness of weld joint. The decrease in hardness is because of*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 71at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 16. formation of coarse grains in the fusion zone. Increase in peak current increases the heat inputwhich results in slow cooling rate, which also contributes to longer time for grain coarsening.Pulsing current helps to increase the hardness by refining the grains at the fusion zone. Themathematical models are developed considering only four factors and five levels (peak current,back current, pulse and pulse width). However one may consider more number of factors and theirlevels to improve the mathematical model.9  Acknowledgments  The authors would like to thank Shri. R.Gopla Krishnan, Director, M/s Metallic Bellows (I)Pvt Ltd, Chennai, India for his support to carry out experimentation work.9  References Asiabanpour. B, Khoshnevis. B, and Palmer. K, (2006), Development of a rapid prototyping system using response surface methodology. Journal of Quality and Reliability Engineering International, 22,No.8, p.919.Bauer.W.K, Parnell. S.G and Meyers. A.D, (1999), Response Surface Methodology as a SensitivityAnalysis Tool in Decision Analysis. Journal of Multi-Criteria decision Analysis, 8, p.162.Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V,(2007), Response surface approach to optimize the pulsed current gas tungsten arc welding parameters of Ti-6Al-4V titanium alloy, METALS and MATERIALS International, 13, No.4,p.335.Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2008), A mathematical model to predict impact toughness of pulsed current gas tungsten arc welded titanium alloy, Int J Adv Manuf Technol, 35, p.852.Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2008), Optimizing pulsed current parameters t o minimize corrosion rate in gas tungsten arc welde titanium alloy, Int J Adv Mnauf Technol, 39, p.474.Balasubramaniam.M, Jayabalan.V, Balasubramaniam.V, (2009), Prediction and optimization of pulsed current gas tungsten arc welding process parameters to obtain sound weld pool geometry in titanium alloy using lexicographic method, JMEPEG,18,p.871.Balasubramanian.M, Jayabalan.V, Balasubramanian.V,(2010) Effect of process parameters of pulsed current tungsten inert gas welding on weld pool geometry of titanium welds, Acta Metall.Sin.(Engl. Lett.),23, No.4,p. 312. 72 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao
  • 17. Balasubramanian.V, Lakshminarayanan.A.K, Varahamoorthy.R and Babu.S, (2009), Application of Response Surface Methodolody to Prediction of Dilution in Plasma Transferred Arc Hardfacing of Stainless Steel on Carbon Steel , Science Direct, 16, No.1,p.44.Balasubramanian.B, Jayabalan.V, Balasubramanian.V,(2006)Optimizing the Pulsed Current Gas Tungsten Arc Welding Parameters, J Mater Sci Technol, 22,No.6, p.821.Barker T B, Quality by experimental design,(1985), ASQC Quality Press, Marcel Dekker.Box G EP, Hunter W H, Hunter J S,(1978), Statistics for experiments [M], New York: John Wiley and Sons, p.112.Cochran W G, Cox G M, (1957), Experimental Designs, John Wiley and Sons Inc, London.Gardiner W P, Gettinby G, (1998), Experimental design techniques in statistical practice, Horwood press, Chichester.Hsiao.Y.F, Tarng.Y.S, and Wang. J,(2008), Huang Optimization of Plasma Arc Welding Parameters by Using the Taguchi Method with the Grey Relational Analysis, Journal of Materials and Manufacturing Processes, 23,p.51.Kechagias. J, (2007), An experiment investigation of the surface roughness of parts produced by LOM process. Rapid Prototyping Journal, 13,No.1, p.17.LakshinarayanaA.K, Balasubramanian.V, Varahamoorthy.R and Babu.S, (2008), Predicted the Dilution of Plasma Transferred Arc Hardfacing of Stellite on Carbon Steel using Response Surface Methodology, Metals and Materials International, 14, No.6,p.779.Madusudhana Reddy G, Gokhale A A, Prasad Rao K, (1997), Weld microstructure refinement in a 1441 grade aluminium-lithium alloy, Journal of Material Science, 32, No.5, p.4117.Montgomery D.C,(1991), Design and analysis of experiments ,3rd Edition, New York, John Wiley and Sons,p.291.Myers, R., and Montgomery. D, (2002), Response Surface Methodology, 2nd ed. Wiley: New York.Mc Clurkin. J.E and Rosen, D.W, (2002), Computer-aided build style decision support for stereo lithography. Rapid Prototyping Journal, 4, No.1, p. 4.Ravindra J, Parmar R S, (1987),Mathematical model to predict weld bead geometry for flux cored arc welding , Journal of Metal Construction, p.45.Siva.K, Muragan.N, Logesh.R,(2008),Optimization of weld bead geometry in Plasma transferred arc hardfacing austenitic stainless steel plates using genetic algorithm, Int J Adv Manuf Technol, Volume 41, Numbers 1-2,p.24.*Corresponding author ( Kondapalli Siva Prasad). Tel/Fax: +91-9849212391. E-mailaddress: kspanits@gmail.com. 2012. American Transactions on Engineering &Applied Sciences. Volume 1 No.1 ISSN 2229-1652 eISSN 2229-1660. Online Available 73at http://TUENGR.COM/ATEAS/V01/57-74.pdf
  • 18. Sheng-Chai Chi, LI-Chang Hsu , (2001),A fuzzy Radial Basis Function Neural Network for Predicting Multiple Quality characteristics of Plasma Arc Welding, IEEE,0-7803-7078-3,No.01,p.2807.Srimanth.N and Murugan.N , (2011), Prediction and Optimisation of Weld Bead Geometry of Plasma Transferred Arc Hardfaced Valve Seat Rings, European Journal of Scientific Research, 51, No2,p.285.Zhang.D.K and Niu.J.T ,(2000),Application of Artificial Neural Network modeling to Plasma Arc Welding of Aluminum alloys, Journal of Advanced Metallurgical Sciences, 13, No.1, p.194. K.Siva Prasad is an Assistant Professor of Department of Mechanical Engineering at Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India. He received his bachelor degree from Osmania University, India and master degree from JNTU, Hyderabad, India. He is also a part time scholar at Andhra University. He is a member of various professional bodies like ISTE, FPSI, ISHRAE etc. His area of research is micro welding processes. Dr. Ch.Srinivasa Rao is an Associate Professor in the Mechanical Engineering Department at Andhra University, Visakhapatnam, India. He obtained his PhD degree from Andhra University, Visakhapatnam, India. He has published his research papers in various International Journals and conferences proceedings. He is a member of various professional bodies like ISTE, IE etc. His area of interest is manufacturing sciences, rapid prototyping and robotics. Professor Dr. D.Nageswara Rao is now Vice Chancellor, Centurion University of Technology & Management, Odisha, INDIA. He obtained his PhD degree from Indian Institute of Technology Delhi, India. He was the coordinator for Centre for Nanotechnology at Andhra University. He has successfully completed various projects sponsored by DST, UGC, AICTE, NRB etc. His area of research is manufacturing sciences and nanotechnology.Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website. 74 Kondapalli Siva Prasad, Ch.Srinivasa Rao, and D.Nageswara Rao